Machine Learning: An Algorithmic Perspective, Second Edition / Edition 2 available in Hardcover
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.
Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
New to the Second Edition
- Two new chapters on deep belief networks and Gaussian processes
- Reorganization of the chapters to make a more natural flow of content
- Revision of the support vector machine material, including a simple implementation for experiments
- New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
- Additional discussions of the Kalman and particle filters
- Improved code, including better use of naming conventions in Python
Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
|Publisher:||Taylor & Francis|
|Product dimensions:||7.10(w) x 10.10(h) x 1.10(d)|
About the Author
Stephen Marsland is a professor of scientific computing and the postgraduate director of the School of Engineering and Advanced Technology (SEAT) at Massey University. His research interests in mathematical computing include shape spaces, Euler equations, machine learning, and algorithms. He received a PhD from Manchester University
Table of Contents
Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial Basis Functions and Splines. Support Vector Machines. Learning with Trees. Decision by Committee: Ensemble Learning. Probability and Learning. Unsupervised Learning. Dimensionality Reduction. Optimization and Search. Evolutionary Learning. Reinforcement Learning. Markov Chain Monte Carlo (MCMC) Methods. Graphical Models. Python.